The effect of simulated winter warming spells on Canada fleabane [Conyza canadensis (L.) Cronq. var. canadensis] seeds and plants
Bibliographic record
Abstract
Experiments were establish at three sites in southern Ontario, Canada in 2009 and 2010 to determine the possible effect of winter warming spells applied in either January, February or March on seed, seedlings, or rosettes of Canada fleabane including effects on winter survival, fecundity, above-ground biomass, and flowering timing. Warming spells reduced survival of fall-established rosettes and fall established seedlings. Warming spells occurring late in winter (March) had a greater effect where March warming spells reduced the survival of rosettes and seedlings on average by 53% and 80%, respectively. In addition, overwintering Canada fleabane plants (rosettes or seedlings) exposed to warming spells flowered earlier (between 29 and71 days earlier). This study also confirms that Canada fleabane seed has little or no dormancy and that the great majority of seed recruits (either in fall or spring) within a given season (between 84% and 93%). We also determined that timing of seed shed in the fall significantly affects the proportion of seedlings emerging either in the spring or fall with late shed favoring seed overwintering and spring seedling emergence. The results of this study suggest that winter warming spells, especially later in the winter (into early spring), may limit the success of Canada fleabane and in particular its success as a winter annual.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".